Staff Machine Learning Engineer, Personalization
New
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SpotifyMusic streaming
For this role, you can be within the North Americas region as long as we have a work location., This team operates within the Eastern Standard time zone for collaboration.Full-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 8+ years of experience building and deploying machine learning systems in production environments.
- Required Skills
- PythonPyTorchAirflowA/B testingLLM
Requirements
- Have 8+ years of experience building and deploying machine learning systems in production environments.
- Bring deep expertise in recommendation systems, ranking models, personalization, or large-scale content discovery platforms.
- Have strong proficiency in Python and hands-on experience building machine learning systems with PyTorch.
- Have experience with large language model training, fine-tuning, evaluation, and optimization, including SFT, distillation, and LoRA.
- Have worked with large-scale inference systems and understand latency, reliability, and cost optimization challenges.
- Know how to design, execute, and interpret online experiments and A/B tests.
- Have experience operating distributed machine learning workloads using Ray, FSDP, HSDP, or similar frameworks.
- Have experience building and maintaining data pipelines and orchestration workflows using Flyte, Airflow, BigQuery, and cloud-based storage platforms.
- Communicate effectively across technical and non-technical audiences and influence technical decisions beyond your immediate team.
Responsibilities
- Own and improve the machine learning models and systems that power the Home feed, including the Shortcuts experience.
- Design, build, and ship personalized recommendations for Spotify listeners.
- Build content recommendation systems for emerging agentic and AI-powered user experiences.
- Train, fine-tune, evaluate, and optimize large language models using techniques such as SFT, distillation, and parameter-efficient training.
- Partner with product managers, engineers, data scientists, and designers to define and execute experimentation strategies.
- Drive A/B testing, monitoring, model evaluation, and continuous optimization of recommendation quality, reliability, and cost efficiency.
- Improve ML platform capabilities, data pipelines, and production systems supporting personalization.
- Drive technical direction in ambiguous problem spaces and contribute to personalization systems architecture.
- Mentor and support other machine learning engineers.
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